The Global Cooling Bet – Part 2

May 13th, 2008 by group

Their figure 4 shows that a standard IPCC-type global warming scenario performs slightly better for global mean temperature for the past 50 years than their new method with initialised sea surface temperatures (see also the correlation numbers given at the top of the panel). That the standard warming scenario performs better is highly remarkable since it has no observed data included. The green curve, which presents a set of individual 10-year forecasts and is not a time series, each time starts again close to the observed climate, because it is initialised with observed sea surface temperatures. So by construction it cannot get too far away, in contrast to the “free” black scenario. Thus you’d expect the green forecasts to perform better than the black scenario. The fact that this is not the case shows that their initialisation technique does not improve the model forecast for global temperature.

Their ‘cooling forecasts’ have not passed a the test for their hindcast period. Global 10-year average temperatures have increased monotonically during the entire time they consider – see their red line. But the method seems to have produced already two false cooling forecasts: one for the decade centered on 1970, and one for the decade centered on 1999.

Their forecast was not only too cold for 1994-2004, but it also looks almost certain to be too cold for 2000-2010. For their forecast for 2000-2010 to be correct, all the remaining months of this period would have to be as cold as January 2008 – which was by far the coldest month in that decade thus far. It would thus require an extreme cooling for the next two-and-a-half years.

Even for European temperatures (their Fig. 3c, not part of our proposed bet), the forecast skill of their method is not impressive. Their method has predicted cooling several times since 1970, yet the European temperatures have increased monotonically since then. Remember the forecasts always start near the red line; almost every single prediction for Europe has turned out to be too cold compared to what actually happened. There therefore appears to be a systematic bias in the forecasts.

One of the key claims of the paper is that the method allows forecasting the behaviour of the meridional overturning circulation (MOC) in the Atlantic. We do not know what the MOC has actually been doing for lack of data, so the authors diagnose the state of the MOC from the sea surface temperatures – to put it simply: a warm northern Atlantic suggests strong MOC, a cool one suggests weak MOC (though it is of course a little more complex). Their method nudges the model’s sea surface temperatures towards the observed ones before the forecast starts. But can this induce the correct MOC response? Suppose the model surface Atlantic is too cold, so this would suggest the MOC is too weak. The model surface temperatures are then nudged warmer. But if you do that, you are making surface waters more buoyant, which tends to weaken the MOC instead of enhancing it! So with this method it seems unlikely to us that one could get the MOC response right. We would be happy to see this tested in a ‘perfect model’ set up, where the SST-restoring was applied to try and get the model forecasts to match a previous simulation (where you know much more information). If it doesn’t work for that case, it won’t work in the real world.

When models are switched over from being driven by observed sea surface temperatures to freely calculating their own sea surface temperatures, they suffer from something called a “coupling shock”. This is extremely hard, perhaps even impossible, to avoid as “perfect model” experiments have shown (e.g. Rahmstorf, Climate Dynamics 1995). This problem presents a formidable challenge for the type of forecast attempted by Keenlyside et al., where just such a “switching over” to free sea surface temperatures occurs at the start of the forecast. In response to the “coupling shock”, a model typically goes through an oscillation of the meridional overturning circulation over the next decades, of the magnitude similar to that seen in the Keenlyside et al simulations. We suspect that this “coupling shock”, which is not a realistic climate variability but a model artifact, could have played an important role in those simulations. One test would be the perfect model set up we mentioned above, or an analysis of the net radiation budget in the restored and free runs – a significant difference there could explain a lot.

To check how the Keenlyside et al. model performs for the MOC, we can look at their skill map in Fig. 1a. This shows blue areas in the Labrador Sea, Greenland-Iceland-Norwegian Sea and in the Gulf Stream region. These blue areas indicate “negative skill” – that means, their data assimilation method makes things worse rather than improving the forecast. These are the critical regions for the MOC, and it indicates that for either of the two reasons 5 and 6, their method is not able to correctly predict the MOC variations. Their method does show skill in some regions though – this is important and useful. However, it might be that this skill comes from the advection of surface temperature anomalies by the mean ocean circulation rather than from variations of the MOC. That would also be a an interesting issue to research in the future.

All climate models used by IPCC, publicly available in the CMIP3 model archive, include intrinsic variability of the MOC as well as tropical Pacific variability or the North Atlantic Oscillation. Some of them also include an estimate of solar variability in the forcing. So in principle, all of these models should show the kind of cooling found by Keenlyside et al. – except these models should show it at a random point in time, not at a specific time. The latter is the innovation sought after by this study. The problem is that the other models show that a cooling of one decadal mean to the next in a reasonable global warming scenario is extremely unlikely and almost never occurs – see yesterday’s post. This suggests that the global cooling forecast by Keenlyside et al. is outside the range of natural variability found in climate models (and probably in the real world, too), and is perhaps an artifact of the initialisation method.